Social Media is Educating Kids on AI: How Can Educators Adapt?
Artificial intelligence (AI) is transforming how students compose essays, practice languages, and fulfill assignments. Educators are also exploring AI for crafting lesson plans, grading assignments, and providing feedback. The rapid developments have left schools, universities, and policymakers struggling to keep pace.
A fundamental question often overlooked in this haste is: how are students and teachers really learning to utilize AI?
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AI in schools — key considerations
Currently, much of this learning occurs informally. Students exchange tips on platforms like TikTok or Discord, and often consult ChatGPT for guidance. Teachers share advice in staff rooms or gain insights from discussions on LinkedIn.
While these networks disseminate knowledge rapidly, they do so inconsistently, seldom prompting deeper reflections on critical issues such as bias, surveillance, or equity. This is where formal teacher education could create impact.
Moving beyond curiosity
Research indicates that educators are ill-equipped for AI. A recent study revealed that many lack the skills to assess the reliability and ethics of AI tools. Professional development often focuses solely on technical training and overlooks broader implications. Meanwhile, uncritical use of AI risks amplifying biases and inequities.
In response, I developed a professional development module within a graduate-level course at Mount Saint Vincent University. Teacher candidates participated in:
- Experiential exploration of AI for feedback and plagiarism detection;
- Collaborative creation of assessments incorporating AI tools;
- Case study analysis of ethical dilemmas in multilingual classrooms.
The aim was to shift from mere experimentation with AI to critical engagement.
Fostering critical thinking in future educators
Throughout the sessions, clear patterns emerged. Teacher candidates began with excitement about AI, which persisted. Participants noted an enhanced ability to evaluate tools, identify biases, and apply AI in thoughtful ways.
Additionally, the vocabulary surrounding AI evolved. Initially, teacher candidates struggled with where to begin, but by the end, they were confidently discussing concepts like “algorithmic bias” and “informed consent.”
Teacher candidates increasingly viewed AI literacy as a matter of professional judgment, interconnected with pedagogy, cultural responsiveness, and their personal educator identity. They recognized literacy not just as grasping algorithms but also as making ethical decisions in the classroom.
The pilot indicates that enthusiasm isn’t the missing element; structured education provided the necessary tools and vocabulary for critical reflection on AI.
(Getty Images/Unsplash+)
Varied approaches
These classroom observations reflect broader institutional challenges. Universities globally have adopted inconsistent policies: some prohibit AI, others tentatively endorse it, while many remain ambiguous. This lack of coherence results in confusion and distrust.
Alongside my colleague Emily Ballantyne, we explored how AI policy frameworks can be adapted for Canadian higher education. Faculty acknowledged AI’s potential, but expressed concerns about equity, academic integrity, and increased workload.
We proposed a model that incorporated a “relational and affective” dimension, highlighting that AI influences trust and the dynamics of teaching relationships, not just efficiency. Practically, this means AI changes not only how assignments are completed but also reshapes interactions between students and instructors in and out of the classroom.
In essence, integrating AI into classrooms alters the relationships between students and teachers and impacts educators’ perceptions of their roles.
When institutions fail to establish clear policies, individual instructors end up functioning as ad hoc ethicists without institutional support.
Embedding AI literacy
However, clear policies alone are insufficient. To ensure AI effectively enhances teaching and learning, institutions must also invest in developing the knowledge and practices that promote critical usage. Policy frameworks offer guidance, but their effectiveness hinges on their implementation in everyday classroom practices.
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Teacher education should take the lead in AI literacy. Given AI’s impact on reading, writing, and assessment, it should not be an optional add-on. Programs must weave AI literacy into curricula and learning outcomes.
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Policies need to be straightforward and practical. Teacher candidates consistently asked: “What are the university’s expectations?” Institutions must differentiate between misuse (e.g., ghostwriting) and legitimate uses (e.g., feedback assistance), as recent research suggests.
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Learning communities are essential. AI knowledge is not static; it evolves alongside changing tools and norms. Faculty circles, curated resources, and interdisciplinary hubs can facilitate strategy sharing and discussions on ethical challenges.
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Equity should be at the forefront. AI tools often carry the biases of their training data and can disadvantage multilingual learners. Institutions should carry out equity audits and ensure that adoption aligns with accessibility standards.
Supporting both students and teachers
Public discussions surrounding AI in classrooms frequently swing between two extremes: enthusiasm for innovation and concerns about cheating. Neither perspective captures the complexities of how students and teachers are genuinely engaging with AI.
While informal learning networks are influential, they often lack the depth needed for ethical reasoning. This is where formal teacher education can guide, enrich, and equalize these skills.
When teachers receive structured opportunities to engage with AI, they transition from passive consumers to proactive shapers of technology. This transformation is crucial, as it ensures that educators are not merely responding to technological shifts but are actively directing the use of AI to support equity, pedagogy, and student learning.
Such agency in education systems is essential if we want AI to enhance rather than undermine learning.
